# PLOD: An Abbreviation Detection Dataset for Scientific Documents

arXiv: https://arxiv.org/abs/2204.12061 · HTML: https://mlsanity.com/paper/2204.12061/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 26 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Maxout Networks](https://mlsanity.com/paper/1302.4389/) (2013)
- [AT-BERT: Adversarial Training BERT for Acronym Identification Winning Solution for SDU@AAAI-21](https://mlsanity.com/paper/2101.03700/) (2021)
- [MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extraction](https://mlsanity.com/paper/2202.09694/) (2022)
- [Acronym recognition and processing in 22 languages](https://mlsanity.com/paper/1309.6185/) (2013)

## Built on by (most cited first, 2 of 2)

- [NameGuess: Column Name Expansion for Tabular Data](https://mlsanity.com/paper/2310.13196/) (2023)
- [Monitoring Transformative Technological Convergence Through LLM-Extracted Semantic Entity Triple Graphs](https://mlsanity.com/paper/2510.25370/) (2025)

## Abstract (arXiv)

The detection and extraction of abbreviations from unstructured texts can help to improve the performance of Natural Language Processing tasks, such as machine translation and information retrieval. However, in terms of publicly available datasets, there is not enough data for training deep-neural-networks-based models to the point of generalising well over data. This paper presents PLOD, a large-scale dataset for abbreviation detection and extraction that contains 160k+ segments automatically annotated with abbreviations and their long forms. We performed manual validation over a set of instances and a complete automatic validation for this dataset. We then used it to generate several baseline models for detecting abbreviations and long forms. The best models achieved an F1-score of 0.92 for abbreviations and 0.89 for detecting their corresponding long forms. We release this dataset along with our code and all the models publicly in https://github.com/surrey-nlp/PLOD-AbbreviationDetection